Geographical POI recommendation for Internet of Things: A federated learning approach using matrix factorization

Huang, Jiwei; Tong, Ze‐Yu; Feng, Zihan

doi:10.1002/dac.5161

Cited by 70 publications

(45 citation statements)

References 27 publications

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“…Its fundamental principle is the main matrix decomposition process, which decomposes the client interaction matrix into items by multiplying two rectangular matrices with lower dimensions. The original matrix with two different latent spaces is represented in the simple matrix decomposition method [123][124][125][126].…”

Section: Poi Recommendation Systems Based On a Matrix Factorization M...mentioning

confidence: 99%

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

2022

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In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work.

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Section: Poi Recommendation Systems Based On a Matrix Factorization M...mentioning

confidence: 99%

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

2022

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“…Where V in Fig. 4 denotes a set of fruit y population location points, i.e., each vertex in the locality sensitive hashing table of fruit y population location, X i denotes any point of population location points in V, Pop denotes the set of population individuals generated according to the population location X i with a scale of 50, and Fitness denotes the tness value of each fruit y individual on the benchmark function, i.e., the Step6-Step7 denotes the new population location selection scheme, i.e., the locality sensitive hashing table model with roulette wheel selection method [12][13][14][15].…”

Section: Lshfoamentioning

confidence: 99%

“…FOA-ESP: This is a baseline approach that tries to solve the edge server placement problem by using the classical FOA only [21][22][23].…”

Section: Lshfoa-espmentioning

confidence: 99%

Locality Sensitive Hashing-aware Fruit Fly Optimization Algorithm and its application in Edge Server Placement

Cao

Liu

Jin

2022

Preprint

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As is well known that global optimization ability of Fruit fly Optimization Algorithm (FOA)is weak because it is easy to fall into local optimum. In this paper a Fruit Fly Optimization Algorithm based on Locality Sensitive Hashing-aware (LSHFOA)was proposed. The locality sensitive hashing mechanism to optimize the generation mechanism for swarm population individuals was used, which can improve the individual diversity of the population. Meanwhile, when the fruit fly population falls into local optimum, the locality sensitive hashing mechanism was adopted to change the population location, which is used for jumping out of local optimal limits. To verify the performance of LSHFOA, it was compared with FOA and its improvement algorithms CFOA, and IFFO with 8 representative benchmark functions. A large number of experimental results showed that LSHFOA has a faster convergence speed and higher precision of optimization for function optimization, especially in high-dimensional multi-peak functions. In the end, we apply this new Algorithm to solve the edge server placement (ESP) problem in the edge computing environment, the experimental results show that the new algorithm has excellent application effect.

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“…In a multi-cloud environment, different service providers provide more heterogeneous resources to complete the tasks submitted by cloud customers. Therefore, the design of task scheduling scheme in a multi-cloud environment, that is, efficient resource management, is a challenging problem [1][2][3]. In the face of this challenging problem, the demand for technical talents related to artificial intelligence multi-cloud scheduling is increasing, which further leads to the change of post setting, talent demand and employment situation in the labor and employment market.…”

Section: Introductionmentioning

confidence: 99%

Research on Influencing Factors of Artificial Intelligence Multi-cloud Scheduling Applied Talents Training based on DEMATEL-TAISM

Bian

Xie

2022

Preprint

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With the rapid development of Internet of things (IoT) technology and the increasing popularity of IoT devices, more and more computing intensive IoT applications came into being. However, due to the limited resources of IoT devices, cloud computing systems are required to compute intensive IoT applications. Further, in order to be subject to a single cloud computing service provider, multi-cloud computing will become an IoT service cloud computing solution. In view of the complexity of multi-cloud scheduling, the application of artificial intelligence will be an important technology to solve the multi-cloud scheduling of IoT. The corresponding talent training plays an important role in the development and implementation of the artificial intelligence multi-cloud scheduling of IoT. Firstly, this paper studies the key influencing factors of IoT’s artificial intelligence multi-cloud scheduling applied talents training. Combined with the characteristics of the development of China’s artificial intelligence industry, this paper summarizes the influencing factors from the four dimensional training path of government departments, universities, enterprises and scientific research institutes. According to the training purpose of artificial intelligence multi-cloud scheduling applied talents training, build an artificial intelligence multi-cloud scheduling applied talents training influencing factor index system. Then, the DEMATEL method is used to establish multiple correlation matrices according to the direct influence correlation between the factors, and calculate the influence degree, affected degree, center degree and cause degree of the factors; Using the improved AISM method, based on the idea of game confrontation, from the two opposite extraction rules of result priority and cause priority respectively, a group of confrontation level topological maps with comprehensive influence values reflecting the interaction of factors are obtained, and relevant suggestions are put forward in order to provide reference for the training of artificial intelligence multi-cloud scheduling applied talents.

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Geographical POI recommendation for Internet of Things: A federated learning approach using matrix factorization

Cited by 70 publications

References 27 publications

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

Locality Sensitive Hashing-aware Fruit Fly Optimization Algorithm and its application in Edge Server Placement

Research on Influencing Factors of Artificial Intelligence Multi-cloud Scheduling Applied Talents Training based on DEMATEL-TAISM

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